flu$created_at <- flu$created_at %>% ymd_hms()

ggplot(data = flu, mapping = aes(x = created_at, y = stat(count))) +
  geom_density() + 
  labs(
    title = "",
    x = "",
    y = "Número de menções"
  )

layout_list <- list(
  list(layout = 'star'),
  list(layout = 'circle'),
  list(layout = 'gem'),
  list(layout = 'graphopt'),
  list(layout = 'grid'),
  list(layout = 'mds'),
  list(layout = 'randomly'),
  list(layout = 'fr'),
  list(layout = 'kk'),
  list(layout = 'nicely'),
  list(layout = 'lgl'),
  list(layout = 'drl'))


bigrams <- flu %>% 
  unnest_tokens(bigram, text, token = "ngrams", n = 2)

stop_words <- tibble(word = stopwords())

bigrams_filtered <- bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>% 
    anti_join(stop_words, by = c("word1" = "word")) %>% 
    anti_join(stop_words, by = c("word2" = "word"))

# new bigram counts:
bigram_counts <- bigrams_filtered %>%
  count(word1, word2, sort = TRUE)

graph <- bigram_counts  %>%
  top_n(300) %>% 
  as_tbl_graph()

## Selecting by n

filtered_graph <- graph %>% 
  mutate(community = group_walktrap()) %>% 
  filter(community %in% 1:3) # Getting rid of tiny communities

layouts <- filtered_graph %>% 
  invoke_map('create_layout', layout_list, graph = .) %>% 
  set_names(unlist(layout_list)) %>% 
  bind_rows(.id = 'layout')

dummy_layout <- create_layout(filtered_graph, 'nicely')

attr(layouts, 'graph') <- attr(dummy_layout, 'graph')
attr(layouts, 'circular') <- FALSE

g <- ggraph(layouts) +
  geom_node_point(aes(col = as.factor(community))) +
  geom_node_text(aes(label = name), vjust = 1, hjust = 1, size = 3.5) +
  theme_graph() +
  theme(legend.position = 'none') +
  labs(title = 'Word relationships',
       subtitle = 'Using {closest_state} layout engine') +
  transition_states(layout, 1, 2) +
  ease_aes('linear') +
  view_follow()

animate(g, fps = 30, nframes = 1000)